code: def test__large_lambda():
      X, y = iris_data()
      clf = LogisticRegression()
      clf.fit(X, y)
  
      x_ref = X[15]
  
      res = create_counterfactual(
          x_reference=x_ref,
          y_desired=2,
          model=clf,
          X_dataset=X,
          y_desired_proba=1.0,
          lammbda=100,
          random_seed=123,
      )
  
      assert np.argmax(clf.predict_proba(x_ref.reshape(1, -1))) == 0
      assert np.argmax(clf.predict_proba(res.reshape(1, -1))) == 2
      assert (
          round(
              (clf.predict_proba(res.reshape(1, -1))).flatten()[-1], 2
          )
          >= 0.96
      )
  
file_path: mlxtend/mlxtend/evaluate/tests/test_counterfactual.py
imports: import numpy
  from sklearn.linear_model import LogisticRegression
  from mlxtend.classifier import OneRClassifier
  from mlxtend.data import iris_data
  from mlxtend.evaluate import create_counterfactual
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 3 for calling a global function with example usage: test__large_lambda() without return types